Search results

The purpose of this research is to study and examine the influence of systematic supply risk management (SRM) on the buyer's firm performance, mainly focussing on the Indian electronics industry.

The study has framed a set of hypotheses on the risk management model. A thorough literature review and experts' opinion were considered in framing constructs and hypothesis for the model. We adopted self-administration questionnaires mainly focusing on the Indian electronics industry. The derived hypothesis is tested using partial least squares (PLS) method from 140 survey data pertaining to small, medium and large scale industries.

Study justify that constructs with high loadings for risk identification, risk assessment, risk reduction, and risk monitoring supports all hypothesized relation to better risk management. The model captures superior risk identification, risk control and risk monitoring for overall firm performance, but fails to justify with organization supply risk assessment process on overall firm performance.

The study mainly focused on SRM process on firm performance. Study mainly focused on single survey responses and expert's perceptions on SRM practices in Indian electronics industry.

Research empirically justifies the effects of SRM process on organization performance. Furthermore, effective SRM practices assist decision makers framing corrective strategies to mitigate risk occurrences and their negative impact.

This empirical work provides a deep understanding of SRM process on the Indian electronics industry and their perception towards firm performance. Moreover, this is one of the few empirical studies addressing SRM practices in the Indian electronics industry.

The purpose of this paper is to present a framework for identifying various inbound supply-risk factors and analyzing its indicators considering the contextual relationship between them. This study additionally proposes a framework for developing an overall inbound supply-risk score considering a real-life case of the electronics supply chain (ESC) in the Indian context.

In total, 32 risk indicators are identified by a systematic literature review approach and are validated by supply chain practitioners/experts and further categorized into six main risk factors. A hybrid multi-criteria decision-making-based DANP (DEMATEL and ANP) framework is employed to develop the overall inbound-supply-risk score (ISRS) and to prioritize the risk indicators. Indian ESC is chosen as a viable case study to demonstrate the effectiveness of the proposed framework.

The outcomes from the study reveal that the overall ISRS in the ESC is 36 percent and additionally forewarns critical inbound-supply-risk factors such as supplier performance, product, and buyer organization. Further, the study also identifies the most significant risk indicators such as price margin, investment, on-time delivery, order fulfillment and design changes for ESC.

Supply chain practitioners can adopt this framework as a useful inbound supply-risk assessment tool. Moreover, the hybrid framework will address subjectivity and interrelations among various factors through experts’ judgments. The results will assist the managers to have better insights on the critical risk factors and their complicated interrelationships and further strategize action plans to nullify the impact of incoming risks. This study mainly focused on risk identification and assessment of electronics inbound-supply-risk indicators in the Indian context. The framework can be used for other manufacturing and service industries, albeit the results derived are in the context of a developing country.

This paper provides an effective risk assessment framework for the supply chain practitioners/managers to develop a decision-support system for inbound-supply-risk quantification and prioritization of risk factors in the context of the ESC.

This paper aims to present a model that maps the relationship between factors and process stages of the knowledge transfer (KT) between new product development (NPD) teams.

The model was constructed through a quantitative evaluation with scholars and practitioners of the NPD field. To evaluate the model and its usefulness, a practical application was carried out in two large Brazilian companies.

A relationship model between 16 main influence factors and 11 stages of the KT process was obtained. It could be seen through the practical applications that the model helps companies to identify and prioritize improvement opportunities in the KT between NPD teams.

Prior studies had been concerned with the analysis of KT factors, but considering KT as an isolated act. So, this work has advanced in the deployment of the KT stages and the relationship of those with different influence factors.

This paper brings an important practical contribution, once several academic works have been concerned with to demonstrate relationships among factors, but few have shown how these results could help to solve practical issues observed in companies.

Results presented in this paper enable the extension of comprehension about the KT phenomena in the NPD process. Furthermore, this paper shows how the proposed model can be used as a decision tool to guide a diagnosis and detect improvement opportunities in KT between NPD teams.

The World Health Organization identified infectious healthcare waste as a threat to the environment and human health. India’s current medical waste management system has limitations, which lead to ineffective and inefficient waste handling practices. Hence, the purpose of this paper is to: first, identify the important barriers that hinder India’s healthcare waste management (HCWM) systems; second, classify operational, tactical and strategical issues to discuss the managerial implications at different management levels; and third, define all barriers into four quadrants depending upon their driving and dependence power.

India’s HCWM system barriers were identified through the literature, field surveys and brainstorming sessions. Interrelationships among all the barriers were analyzed using interpretive structural modeling (ISM). Fuzzy-Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis was used to classify HCWM barriers into four groups.

In total, 25 HCWM system barriers were identified and placed in 12 different ISM model hierarchy levels. Fuzzy-MICMAC analysis placed eight barriers in the second quadrant, five in third and 12 in fourth quadrant to define their relative ISM model importance.

The study’s main limitation is that all the barriers were identified through a field survey and barnstorming sessions conducted only in Uttarakhand, Northern State, India. The problems in implementing HCWM practices may differ with the region, hence, the current study needs to be replicated in different Indian states to define the waste disposal strategies for hospitals.

The model will help hospital managers and Pollution Control Boards, to plan their resources accordingly and make policies, targeting key performance areas.

The study is the first attempt to identify India’s HCWM system barriers and prioritize them.

This research aims to study the students' perspectives on synchronous and asynchronous learning during the COVID-19 Pandemic. Both synchronous and asynchronous learning approaches used in online education have positive and negative outcomes. Hence, the aim is to study online education's positive and negative consequences, reflecting sync and async approaches. This research followed a mixed research approach. The key stakeholders of this research are the Indian educational institutions and students.

This research collected data from the students undergoing synchronous and asynchronous learning amidst the COVID-19 Pandemic. The data were collected (N = 655) from various students taking online classes during the pandemic. A questionnaire survey was distributed to the students through online platforms to collect the data. In this research, the authors have collected data using simple random sampling, and the same has been used for data analysis using SPSS version 26. The collected data were exposed to a factor analysis using a principal component analysis technique to reduce the vast dimensions.

The study findings show that synchronous learning is sometimes stressful, placing more responsibility on students mainly because of the increased screen time. At the same time, asynchronous learning allows the students to self-explore and research the topics assigned to them. Students also felt that asynchronous activities create a burden because of many written assignments to be submitted within a short period. Overall, the COVID-19 pandemic has been challenging for the students and the teachers. However, teachers have helped students to learn through digital platforms. The majority of the respondents opined that technological disruptions and death in the family circle had been significant reasons for not concentrating during online classes. However, the combination of synchronous and asynchronous learning has led to a balanced education.

Higher education has undergone multiple transformations in a short period (from March 2020, 2021 and beyond). Educational institutions underwent a rapid transition in remote teaching and learning in the initial stages. As time progressed, educational institutions did course navigation where they relooked into their course plans, syllabus and brought a structural change to match the pandemic requirements. Meanwhile, educational institutions slowly equipped themselves with infrastructure facilities to bring academic integrity. At present, educational institutions are ready to face the new normality without disrupting services to society.

Educational institutions create intellectual capital, which is important for the development of the economy. In the light of COVID-19, there are new methods and approaches newly introduced or old methods and approaches, which are reimplemented, and these approaches always work for the benefit of the student community.

The authors collected data during the COVID-19 pandemic; it helped capture the students' experience about synchronous and asynchronous learning. Students and faculty members are newly exposed to synchronous and asynchronous learning, and hence, it is essential to determine the outcome that will help many stakeholders.

The idea of relating health, education and other social sector variables with growth and development, or trying to find any convergence among similar countries with respect to such macroeconomic indicators is the basic idea of this chapter, which explores the concept of enhancing welfare through a subjective route, specifically by way of expenditure in health care. By way of testing σ- and β-convergence of current healthcare expenditure per capita of the BRICS countries for the period 2006–2018, it has been found that these countries catch up with significant convergence. It has also been obtained through panel data analysis that such convergence is significantly explained by gross domestic product (GDP) per capita, life expectancy at birth, elderly ratio, CO₂ emissions and prevalence of undernourishment, all of which are in accordance with conventional hypotheses. This chapter claims that, by way of convergence, the BRICS nations may emerge as a new economic power, and expenditure on health care is one of the major areas, among other social sector variables that will play an important role.

During the late 1900s, there was a notable trend of rapid urbanization worldwide, coinciding with a rise in atmospheric carbon dioxide levels. Agricultural production driven by expanded trade is recognized as a major contributor to global pollution and biodiversity depletion. Additionally, tropical deforestation resulting from agricultural activities significantly impacts global greenhouse gas emissions. This chapter aims to examine the interrelationships between crop production, livestock production, transport services, and CO₂ emissions from 1998 to 2019. To achieve this goal, the study begins by conducting stationary tests to determine the order of integration for the variables under consideration. Following this, panel unit root tests are employed, and subsequently, panel cointegration tests are conducted to identify any long-term relationships among the selected variables. The findings reveal a significant long-term relationship among crop production, livestock production, transport services, and CO₂ emissions.

This paper aims to present the design and implementation of an Internet of Things (IoT)-based e-learning testbed using Raspberry Pi mounted on Raspbian operating system (OS).

The testbed is composed of five Raspberry Pi B+ computers. The experiments are carried out in the department floor considering an non line of sight (NLoS) environment. Single constant bit rate (CBR) flows were transmitted over user datagram protocol (UDP), and data were collected for five metrics: throughput, packet delivery ratio (PDR), hop count, delay and jitter using the Iperf.

The implemented testbed was evaluated using experiments. The experimental results showed that the nodes in the testbed were communicating smoothly, and by using attention value, the learner concentration is increased.

The performance of the Optimized Link State Routing (OLSR) protocol was analyzed in a floor environment considering the NLoS scenario. However, this testbed can be implemented to other protocols also.

Because of the opportunities provided by the internet, people are taking advantage of e-learning courses, and enormous research efforts have been dedicated to the development of e-learning systems. To date, many e-learning systems are proposed and used practically. However, in these systems, the e-learning completion rate is low. To deal with this problem, an IoT-based e-learning system was implemented to increase the e-learning completion ratio by increasing the learner concentration.

It is an established document at the global level that more environmental pollution is associated with more growth of the output for some economies, and on the other hand, more pollution is associated with low growth of output in some other economies. Having the scenario of massive climate change, the global policymakers have been experimenting with drastic reductions in the use of energy and fossil fuels within the target year on 2030 for some countries and 2050 for other countries. The negative impacts of the environmental pollution and the pollution abating targets influence the decision-making processes of the households and business houses around the world. There is thus a potential nexus between the emissions of different greenhouse gases (GHGs) and consumers' and business sentiments in taking optimum decisions. Under this background, this chapter aims to investigate whether GHG emissions and consumers' and business sentiments have any association and interplays for the period 1998–2019 using appropriate econometric exercises. The correlation shows positive results with consumers' sentiments in some developing countries but negative results with business sentiments in some developed countries. Further, there are long-run relationships of GHG with consumers' sentiments and business sentiments in some countries with the mixed results of causal interplays.